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Feature Availability Numeric Distribution and IQR Outlier Flags
x402
Feature Availability Numeric Distribution and IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).
Computes numeric distribution statistics and IQR-based outlier flags for up to 200 feature availability records across two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feature availability — Numeric distribution + IQR outlier flags: Numeric distribution + IQR outlier flags for supplied feature availability records (populated_features, expected_features). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a numeric distribution profile (e.g. min, max, mean, median, quartiles, std dev) and (2) IQR-based outlier flags per row for the selected field (populated_features or expected_features), covering up to 200 supplied rows.
Example use cases
ML pipeline feature completeness audit
“I have up to 150 records of daily feature availability from our ML pipeline, each with a row_id, dataset_group, date, populated_features, and expected_features. Can you compute the numeric distribution and flag IQR outliers on the populated_features column so I can see which days had abnormal feature population rates?”
Data quality monitoring for dataset groups
“We track expected_features and populated_features across two dataset groups every day. Can you run the numeric distribution and IQR outlier analysis on these 80 rows using the expected_features field so we can spot which dataset groups consistently under-deliver on feature counts?”
Ad-hoc feature availability anomaly detection
“I've got 200 rows of feature availability records with dates, row IDs, and both populated and expected feature counts. Please analyze the populated_features column for its distribution stats and flag any IQR outliers so I can investigate the worst offenders before our model retraining run.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-10-080?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 feature availability records and need both a numeric distribution summary and IQR-based outlier detection in a single call for $0.01 USDC. It is ideal for lightweight, offline statistical profiling of dataset quality metrics without requiring a live data connection or external ML infrastructure. Prefer it over general-purpose analytics APIs when your input fits the structured row schema with populated_features and expected_features fields and you need two bundled reports cheaply.
Side effects
- Fewer than 1 row supplied returns an error
- More than 200 rows causes rejection — batch must be split
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Null values in the selected field may reduce report coverage or skew distribution
- Missing required fields (row_id, date, populated_features, expected_features) cause validation failure
- Payment not attached or insufficient USDC causes 402 rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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